Skip to main content


Log in

Pattern recognition in genetic sequences by mismatch density

  • Published:
Bulletin of Mathematical Biology Aims and scope Submit manuscript


A new development is introduced here in the use of dynamic programming in finding pattern similarities in genetic sequences, as was first done by Needleman and Wunsch (1969). A condition of pattern similarity is defined and an algorithm is given which scans any set of similarities and screens out those which fail to meet the condition. When the set to be scanned contains every pair of segments, one from each of two given sequences of lengthsm andn (i.e. every possible location for a pattern similarity), then it completes the scan in a number of computational steps proportional tom·n, leaving those pairs of segments which satisfy the similarity condition. The algorithm is based on the concept of match density, as suggested by Goad and Kanehisa (1982).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others


  • Goad, W. B. and M. I. Kanehisa. 1982. “Pattern Recognition in Nucleic Acid Sequences. I. A General Method for Finding Local Homologies and Symmetries.”Nucl. Acids Res. 10, 247–278.

    Google Scholar 

  • Gordon, L., M. F. Schilling and M. S. Waterman (1984). “An Extreme Value for Long Head Runs.” Reprint, Dept. of Math., University of Southern California.

  • Needleman, S. B. and C. D. Wunsch. 1969. “A General Method Applicable to the Search for Similarities in the Amino Acid Sequences of Two Proteins.”J. Mol. Biol. 48, 443–453.

    Article  Google Scholar 

  • Sellers, P. H. 1974a. “An Algorithm for the Distance between Two Finite Sequences.”J. Combinat. Theor. 17, 253–258.

    Article  MathSciNet  Google Scholar 

  • — 1974b. “On the Theory and Computation of Evolutionary Distances.”SIAM J. appl. Math. 26, 787–793.

    Article  MATH  MathSciNet  Google Scholar 

  • — 1980. “The Theory and Computation of Evolutionary Distances: Pattern Recognition.”J. Algorithms 1, 359–373.

    Article  MATH  MathSciNet  Google Scholar 

  • Smith, T. F. and M. S. Waterman. 1981. “Identification of Common Molecular Subsequences.”J. Mol. Biol. 147, 195–197.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Rights and permissions

Reprints and permissions

About this article

Cite this article

Sellers, P.H. Pattern recognition in genetic sequences by mismatch density. Bltn Mathcal Biology 46, 501–514 (1984).

Download citation

  • Issue Date:

  • DOI: